We consider a repeated Prisoner's Dilemma game where two independent learning agents play against each other. We assume that the players can observe each others' action but are oblivious to the payoff received by the other player. Multiagent learning literature has provided mechanisms that allow agents to converge to Nash Equilibrium. In this paper we define a special class of learner called a conditional joint action learner (CJAL) who attempts to learn the conditional probability of an action taken by the other given its own action and uses it to decide its next course of action. We prove that when played against itself, if the payoff structure of Prisoner's Dilemma game satisfies certain conditions, using a limited exploration technique these agents can actually learn to converge to the Pareto optimal solution that dominates the Nash Equilibrium, while maintaining individual rationality. We analytically derive the conditions for which such a phenomenon can occur and have shown experimental results to support our claim.

Peer-to-peer (P2P) systems enable users to share resources in a networked environment without worrying about issues such as scalability and load balancing. Unlike exchange of goods in a traditional market, resource exchange in P2P networks does not involve monetary transactions. This makes P2P systems vulnerable to problems including the free-rider problem that enables users to acquire resources without contributing anything, collusion between groups of users to incorrectly promote or malign other users, and zero-cost identity that enables nodes to obliterate unfavorable history without incurring any expenditure. Previous research addresses these issues using user-reputation, referrals, and shared history based techniques. Here, we describe a multi-agent based reciprocity mechanism where each user's agent makes the decision to share a resource with a requesting user based on the amount of resources previously provided by the requesting user to the providing user and globally in the system. A robust reputation mechanism is proposed to avoid the differential exploitations by the free-riders and to prevent collusion. Experimental results on a simulated P2P network addresses the problems identified above and shows that users adopting the reciprocative mechanism outperform users that do not share resources in the P2P network. Hence, our proposed reciprocative mechanism effectively suppresses free-riding.

The unprecedented scale of IT service delivery requires careful analysis and optimization of service systems. Simulation is an efficient way to handle the complexity of modeling and optimization of real-world service delivery systems. However, typically developed custom simulation models lack standard architectures and limit the reuse of design and implementation artifacts across multiple models. In this work, following the design science research methodology, based on a formal model of service delivery systems and applying an adapted Software Product Line (SPL) approach, we create a design artifact for building product lines of IT service delivery simulation models, which vastly simplify and reduce the cost of simulation model design and development. We evaluate the design artifact by constructing a product line of simulation models for a set of IBM's IT service delivery systems. We validate the proposed approach by comparing the simulation results obtained using our models with the results from the corresponding custom simulation models. The case study demonstrates that the proposed approach leads to 5-8 times reductions in the time required to design and develop related simulation models. The potential implications of the application of the proposed approach within an organization are quicker responses to changes in the business environment, more information to assist in managerial decisions, and reduced workload on the process re-engineering specialists.

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This paper explores the possibility of using mobile sensing data to detect certain in-store shopping intentions or behaviours of shoppers. We propose a person-independent activity recognition technique called CROSDAC 1 , which captures the diversity in the manifestation of such intentions or behaviours in a heterogeneous set of users in a data-driven manner via a 2-stage clustering-cum-classification technique. Using smartphone based sensor data (accelerometer, compass and Wi-Fi) from a directed, but real-life study involving 86 shopping episodes from 30 users in a mall's food court, we show that CROSDAC's mobile sensing-based approach can offer reasonably high accuracy (77.6% for a 2-class identification problem) and outperforms the traditional communitydriven approaches that unquestioningly segment users on the basis of underlying demographic or lifestyle attributes.